Convoy Leverages Machine Learning to Help Shipping Industry Reduce CO2 Emissions

Convoy Inc. (Convoy), a digital freight network, uses machine learning (ML) on AWS to help the shipping industry increase its sustainability and reduce CO2 emissions. More than three million professional truck drivers travel over 95 billion miles across the U.S. each year. About 35 percent of the miles logged are from empty trucks. These empty miles produce about 76 million metric tons of CO2 emissions annually and typically accrue while the driver is en route to the next pick up.

Convoy, based in Seattle, uses Amazon SageMaker for ML to help the shipping industry increase efficiency and reduce empty miles to zero.

Aaron Terrazas, director of economic research at Convoy, and Jennifer Wong, head of sustainability marketing at Convoy, recently shared more on the AWS Fix This podcast about shipping, sustainability, and how the cloud powers Convoy’s work.

On the episode, “Zero Waste Trucking,” Terrazas and Wong explain how the Convoy digital freight network uses ML to connect shippers that have goods to be delivered with drivers who are looking for loads to haul. Convoy bundles multiple shipments into a single job for a driver to reduce empty miles by maximizing the amount of time trucks travel full.

Drivers who use choose bundled shipments, called “automated reloads,” on average, travel with a full truck over 80 percent of the time. Convoy estimates that if the shipping industry as a whole were to achieve the same efficiency improvements as automated reloads, it could reduce CO2 emissions by 32 million metric tons each year.

Listen to Fix This to learn more about how Convoy is helping the shipping industry “keep on trucking” while lowering costs, reducing waste, and becoming more sustainable.

Fix This is available for streaming and download on Apple Podcasts, Google Play, Spotify, Stitcher, TuneIn, Overcast, iHeartRadio, and via RSS.